manufacturing efficiency

Use of machine learning and artificial intelligence (ML/AI) is on an exponential rise across fields1 including all aspects of the semiconductor industry. In the last decade, the use of ML/AI exploded in the areas of speech recognition, facial recognition, smart phone features, search engines and now large language models like ChatGPT, Bard AI, and CoPilot. The ML/AI growth has been enabled by massive data storage capacity and increased compute performance, leading to projections for the semiconductor industry to reach over $1 trillion in annual revenue by 2030, with about 50% of the industry’s growth related to GenAI2. Figure 1: McKinsey Company on GenAI driving semiconductor industry growthAs semiconductor manufacturing drives toward Industry 4.0, SEMI member companies have a vision of Industry 5.0, truly adaptive manufacturing, integrating human creativity with robotic precision enabled by AI. Along that path, automation and data exchange in every step of manufacturing is essential, with data acquisition, data integrity and relevance, and operational Digital Twins3 as defined steppingstones to the factory of the future.Based on growing member interest in ML/AI, in 2019, SEMI assembled technology communities that quickly engaged in AI discussions and proofs of concept, discovering gaps in the path to Industry 4.0. Successful demonstrations of the value of AI in chip manufacturing process development and factory efficiency, not to mention GenAI uses in society, hastened the pace to produce faster, more powerful chips to accommodate the computation and communication requirements. Recognizing the industry opportunity and the mounting role AI plays in the semiconductor supply chain, SEMI initiated several thought leadership efforts, namely the Smart Manufacturing Initiative, Smart Data-AI Initiative, and the Future of Computing think tank.Smart Manufacturing According to the SEMI World Fab Forecast, over 100 new and expanded wafer fabs will begin volume production by 2027. This massive capacity expansion will need to achieve the highest possible operational efficiency and performance. To this end, the Smart Manufacturing Initiative is a technology community with over 120 member companies collaborating pre-competitively to transform manufacturing. The SEMI Smart Manufacturing Global Executive Committee (GEC), outlined a roadmap vision for the cognitive factory of the future based-on technology, sustainability and future talent. The GEC has been working with members to realize that vision. Figure 2 describes this vision in terms of the technology progression needed and the approximate timeline for implementation by most manufacturers. The proliferation of this vision through Smart Manufacturing Forums at SEMICON events around the globe, newsletters and blogs has garnered enormous interest and participation in the initiative and is central to the mission of connecting and raising awareness within the ecosystem. Figure 2: AI-Driven Smart Factory (Point Systems to Autonomous Solutions) To move the needle on this vision, industry experts in the initiative successfully created and launched the Industry 4.0 Readiness Assessment Model (IRAM) to help assess technology deployment progress. IRAM adoption is steadily growing. Modern front-end and back-end lines produce an extraordinary amount of multi-modal data from a variety of sources, and this is key to success in unlocking the potential of AI in manufacturing environments. The initiative’s global working groups on Data Architectures and Smart Control Room among others are working towards a holistic Cognitive Factory framework uniting the vertical and horizontal flow of information. Integral to the Cognitive Factory are smart manufacturing standards, that will accelerate the vision outlined above, and without which local solutions are unlikely to scale.In 2023, the Smart Manufacturing Initiative brought together industry leaders in a unique Digital Twin workshop to align on the state of semiconductor development and usage. The key takeaways from this workshop are captured in a white paper that highlighted the need to accelerate efforts in multiple areas including standards. Along with SEMI International Standards, Smart Manufacturing supports other standards development organizations (SDOs) and NIST standards development, for example, to identify and drive critical standards for Cognitive Factory implementation. The initiative is planning future workshops on Cognitive Factory Framework requirements, Digital Twins, and Smart Data AI in the coming months. that highlighted the need to accelerate efforts in multiple areas including standards. Along with SEMI International Standards, Smart Manufacturing supports other standards development organizations (SDOs) and NIST standards development, for example, to identify and drive critical standards for Cognitive Factory implementation. The initiative is planning future workshops on Cognitive Factory Framework requirements, Digital Twins, and Smart Data AI in the coming months.The GEC has identified critical interrelationships in addition to the technology focus. At the intersection with sustainability, the initiative has formed a collaborative task force with the SEMI Semiconductor Climate Consortium (SCC) to develop a bottom-up technology roadmap that can be used as a blueprint for device makers to meet their proclaimed sustainability goals faster. The task force organized a technical session at SEMICON West 2024 and will be releasing a white paper in the near future. Similarly, the initiative is working with the SEMI Foundation to identify necessary future skills and to make training available through SEMI University. Smart Data AI – Applying AI to Semiconductor OperationsSEMI’s Smart Data-AI Initiative started by assembling a group of interested companies to explore the pivotal role AI could play in the industry and to address the criticality of data. All stakeholders agreed that a formidable challenge was (and still is) the integrity of that data and the security of sharing that data, which is considered IP to most. The optimal implementation of ML/AI techniques can only be gained by access to the comprehensive data set which is owned by numerous supply chain partners. Consequently, semiconductor R D, process and design have not yet realized the full benefit of Data-AI advances. In response, the initiative developed a framework to create value for members and support industry progress. Four pillars underpinning the strategy are:Educating stakeholdersBuilding communitiesExecuting proof-of-concept projectsDeveloping industry standardsTo explore the data challenges the subject matter experts highlighted, a collaborative proof-of-concept (POC) project was proposed in 2019 and accepted by the initiative's partners at Army Research Laboratories4 along with academic and industry partners. The project has completed two phases and is starting on its third phase under the expert guidance of an Industry Advisory Council (IAC) comprised of leaders in the Smart Data-AI community.The POC project, being conducted by principal investigators at Cornell University, demonstrated significant accomplishments from the first two phases, including:An AI model to predict device geometry by optimizing photolithography and plasma etching processesInitial demonstration of secure data-sharing techniques with software-hardware co-optimizationInnovative metrology ideas to train AI algorithms rapidlyStudents trained in cross-disciplinary skills to address the industry’s critical talent shortageFurthermore, the visionary objectives laid out at the initial stages of the POC proved to be synergistic with the strategic goals of the CHIPS Act5, which articulates the need for “collecting, aggregating, and sharing data sets that enable benchmarking and operational improvements, tools development, the creation of digital twins, and training AI models,” and that “the NSTC could develop a methodology for the voluntary sharing of data that protects the proprietary component and national security while enabling access to appropriate performance data.” Phase 3, to be completed by August 2025, will advance the state-of-the-art toward the following specific objectives:A framework to create and integrate Digital Twins of semiconductor R D and manufacturing process toolsAbility to explore processes and generate virtual devices swiftlyDefined interfaces to combine models for each process module or toolAccurate AI-based models for executing virtual process flows to build virtual devicesAdvanced solutions for secure data-sharing across the ecosystem – for example, federated learning where raw data is protected for each entity by building models locally, and only the outputs of the local models are used to build flow-level AI modelsFoundation for future industry standards for secure data-sharing and for interfaces in the virtual innovation environmentSEMI continues to build the collaborative community for Data-AI and strives to synergize with broader efforts such as the Digital Twin Manufacturing Institute, NSTC, and NAPMP in the U.S., and international standards development. Smart Data AI – System-level Innovation for AI – Future of ComputingThe cross-collaborative and synergistic objectives of Smart Manufacturing, the Smart Data-AI proof-of-concept work, and SEMI Standards merge to advance the state-of-the-art. The objective is to help members realize the full value of technology and innovation. In addition to improving semiconductor operations using AI, the efforts also strive to enable SEMI members to participate in, and ultimately profit from, market growth opportunities. Continued progress in AI is crucial both for the industry’s march towards $1 trillion in annual revenue, and for continuing to realize AI’s benefits to society.There are some hurdles to overcome in such a dynamic market. AI models, and the data they process, are outpacing hardware advances, posing a major roadblock for continued progress. As GenAI becomes more pervasive, the performance and power challenges continue to multiply, and require significant innovation in both hardware and software. While individual companies will develop competitive products in this domain, the entire ecosystem needs to evolve in a synergistic manner. As a global industry association, SEMI can play an important role in ensuring this. SEMI started a series of workshops and technology sessions to develop the community and identify opportunities and challenges. The first in this series was a joint workshop with McKinsey Co., held in October 2023, with a focus on innovations in “Domain-Specific Architectures.” Strategically, it brought together thought leaders from three diverse communities - start-ups, investors, and SEMI member companies across the supply chain. This was followed by an overcapacity audience at the Future of Computing session at SEMICON West 2024, where we explored AI-specific hardware with leaders in academia and industry. The Initiative’s next planned event in October 2024 is a focused workshop that is designed to be highly interactive and bring together visionaries and thought leaders from across the value chain – materials, devices, architectures, algorithms, and critical enabling technologies such as photonics, chiplets, advanced packaging, and 3D and heterogeneous integration. The overarching goal is to identify pre-competitive collaborative actions that would help the entire industry. The “Future of Computing” is the broad path to the industry’s future success. While AI systems are the current major wave on this path, future waves may be about heterogeneous integration of photonics and other components, and ultimately, quantum technologies joining the mainstream. SEMI continues to monitor these future trends, strengthen the ecosystem and enable innovation through pre-competitive collaboration, and accelerate implementation through standards.SEMI is fostering today’s collaborations while helping the industry navigate the future of electronics.Melissa Grupen-Shemansky is CTO at SEMI, Pushkar Apte is a Strategic Technology Advisor and Leader of the SEMI Smart Data-AI Initiative, and Mark da Silva is Senior Director of the SEMI Smart Manufacturing Initiative.Definitions and References:1https://arxiv.org/abs/2405.15828 Eamon Duede, William Dolan, Andre Bauer, Ian Foster, Karim Lakhani2McKinsey Company3Digital Twins for semiconductor manufacturing operations are dynamic, predictive, data-driven virtual models of a physical asset, process, or an entire factory, constantly synchronized with its real-world counterpart through real-time data streams and analytics4Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-19-2-0345. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.5“A Vision and Strategy for The National Semiconductor Technology Center (NSTC)” published by the CHIPS R D Office.

Leveraging predictive modeling and other efficiency-enhancing innovations, Digital Twins promise to optimize semiconductor design, manufacturing processes and equipment maintenance while improving overall operational efficiency.

The costs of production are typically based on labor and materials and define manufacturing expenses. But is this approach accurate enough? What about the cost of poor quality and lack of efficiency in production? How is the pandemic impacting semiconductor manufacturing and what can we expect from the future?SEMI recently spoke with Dr. Eyal Kaufman, founder and CEO of QualityLine, a Kiryat Gat, Israel-based provider of smart manufacturing analytics solution, about manufacturing controls and how to select the best data source to improve product quality and yield. Kaufmann provided a snapshot of current best practices used by the company to improve manufacturing efficiencies and product quality while reducing costs. He also discussed the COVID-19 pandemic’s impact on semiconductor smart manufacturing and how artificial intelligence (AI) can help keep factory workers safe.For additional insights on smart manufacturing, join the virtual SEMI Global Smart Manufacturing Conference, October 20 - 22, 2020. Registration is open.SEMI: Real manufacturing costs are calculated based on different aspects such as failures in production, repairs, products returned, scrap of components or late deliveries. Lack of quality and efficiency in manufacturing can undermine a business. How are you helping businesses overcome these challenges?Kaufman: To increase profit margins, it is essential to identify inefficiencies and what improvements to prioritize. Once manufacturing quality and efficiency deficiencies have been measured, the next step is to continuously collect manufacturing data in order to run the final cost analysis and use the analytics to improve the manufacturing process.Smart manufacturing makes it possible to detect anomalies in automated factories, improve production performance and increase profitability. Today, automated data are collected from every machine and piece of test equipment in the factory. Still, manufacturing data collection in many industries remains manual and expensive because of the time and human resources involved. A real-time analytics system can automatically collect all data sources and select the relevant data for analysis, which today is the most accurate and effective way of measuring and resolving quality and efficiency deficiencies.Data-driven decisions made by smart manufacturing reduce costs and improve manufacturing strategies, enabling factory operators to increase product quality, drive higher production capacity and enhance product design for manufacturability. Analytics solutions monitor shop floor operations accessing vendors and subcontractors’ products criterion to run root cause analysis. All those data will reduce the return rate of faulty products and accelerate return on investment. This is why we definitely need smart manufacturing technologies!SEMI: Data accumulated during the manufacturing process includes vital information about failures, anomalies and machine usability. What data are necessary to create the best analytics solution?Kaufman: Many companies today run data mapping and automatic creation of data capture. They often wonder if they need to use testing data, sensors data or product design data, or whether they should collect feedback from their customers and vendors. The best way to create an effective manufacturing analytics system is to use data sources such as: Feedback from customers (returned units, customers complaints, etc..) Testing data from automated test equipment and manual test activities Feedback from technicians repairing faulty units Analysis of testing processes done by vendors Sensors data Data from our ERP/MES systems Artificial intelligence enables any type and size of data structure, even accumulated data, to be automatically integrated and interpreted. AI-based analytics can also establish correlations between each manufacturing stage to help factory operators quickly conduct deep diagnostic and root cause analysis for problem solving and prevention – all while leaving intact a factory’s existing process, machinery and data output. Machine learning evaluates how a factory runs its database and puts all the information generated into an analytics solution that provides the know-how to continuously improve factory efficiency.SEMI: How do you select the best data source to improve manufacturing quality and yield? Kaufman: The accuracy and integrity of data accumulated in our manufacturing process is key to controlling and improving yield and quality while reducing manufacturing costs. Smart manufacturing is a technology-driven approach that uses digital and remote connected machinery to monitor the production process. The goal is to identify anomalies in manufacturing processes and leverage analytics to improve process yield and product quality.To select the relevant data, we collect each type and source of data that can improve the efficiency of a real manufacturing cell: Test data from Automated Testing Equipment Test data from Manual Testing Processes Analyses of repairing processes (failed units during the manufacturing process and units that were returned from customers) Once the data structure is collected, the next step is to turn it into actionable information in the manufacturing process. QualityLine smart manufacturing solutions provide a complete one-stop solution to interpret any manufacturing data structure. Our advanced manufacturing analytics solution detects quality and yield anomalies to reveal production line inefficiencies and opportunities to improve manufacturing quality and efficiency.SEMI: How would you describe your approach?Kaufman: Industry 4.0 in manufacturing claims to be the fourth generation of the industrial revolution. Advanced technologies like manufacturing intelligence and machine learning can efficiently achieve zero defects on manufacturing lines. Digital factories leverage technologies and methodologies including: Big data Self-optimization Self-configuration Self-diagnosis Cognitive and machine learning Smart manufacturing technologies enhance the manufacturing process by continuously collecting and analyzing data in real-time to achieve and maintain high quality performance. The goal is to achieve a significant increase in efficiency and yield while reducing waste and inefficiency.Until now, there has been no viable way to integrate all saved manufacturing data into a unified database. QualityLine advanced manufacturing analytics make it possible for any factory to become digital without installing new hardware, which can be expensive and require not only the extensive integration of existing data but investments in training. Our user-friendly solution integrates manufacturing data for industries with zero automation by first collecting and analyzing data from any type of manual test procedure and then integrated it into manufacturing analytics to improve efficiency.SEMI: Why are Pass/Fail criteria insufficient for controlling manufacturing yield and quality?Kaufman: Managing a mass manufacturing process is always a challenge because hundreds of tasks must be successfully completed before products can ship to customers. At QualityLine, we establish a test process for each stage of the production flow, from the incoming raw material to the final stage prior to the delivery of finished goods to the client. To prevent unexpected downtime incidents, waste and defective products, we collect and interpret every type of relevant data and turn it into meaningful information, setting up the following capabilities: Collection and interpretation of test and process data of each single unit and from each process and plant Automatic detection of quality and yield problems Accurate and quick root cause analysis process Automatic alerts to abnormal issues Prediction process potential and level of failures Measurement of key performance indicators Many manufacturers base their test criteria of each parameter on one key indicator – Pass or Fail. If the test result shows a Pass, then the unit is ready to move on to the next manufacturing stage. If the test result shows Fail, then the unit is sent to a technician for further analysis.A simple Pass or Fail criteria for product quality is far from sufficient since it provides little or no information about edge cases, where one or more of the technical parameters of the unit under test is only within its allowed tolerance. Edge cases may lead to unit failure during operation such as in extreme environments (cold, heat, humidity, electrical overload, impact, etc.). In fact, when running a mass manufacturing line, it is impossible to continuously digest all the detailed information collected from testing stations. Data is analyzed in detail only when a critical quality problem emerges and further analysis is required to understand the root cause.Information overload and the disregard of important parameters makes it hard to control the process and improve quality and yield. New technologies make fast and scalable data integration possible so data can be collected in real time to detect quality issues early, identify complex process disruptions to avoid delivery delays and ensure the best possible product for customers. Only by accurately analyzing data as actionable information can factory operators control the manufacturing quality process.SEMI: How has COVID-19 impacted the smart manufacturing market? How has your technology helped factories remain online?Kaufman: Smart manufacturing is playing a significant role by helping manufacturers overcome COVID-19 challenges such as workforce reductions, social distancing, drops in sales for some specific products and extreme pressure to cut operational costs.Manufacturing leaders turned to us for a solution to the challenges of maintaining efficient factory operations with a limited workforce and reduced number of operating hours. Filling factory orders with fewer people on the floor is a struggle. Digital factory technologies enable remote monitoring of operations to increase efficiency and capacity. We are helping our clients improve efficiency while reducing costs. Our remote monitoring technology can provide the operational visibility to floor managers and engineering teams who cannot go physically to the factories due to safety restrictions. With our advanced manufacturing analytics, they have full end-to-end visibility and can remotely diagnose and solve production line issues. During this critical time, we are proud to be improving remote monitoring solutions to help the industry withstand the pandemic. Some of our clients would have closed their factories otherwise. We’ve been working to integrate manufacturing data in factories that were previously unautomated to drive high automation levels. Integrating processes with existing factory data, regardless of customer’s protocols or automation level, is our great technology advantage.SEMI: How will manufacturing and its supply chains look after COVID-19?Kaufman: Smart manufacturing is currently a necessity. We collect and analyze data not only to improve quality but to reduce client returns of faulty products by 50% and reduce waste by 22%, both critical points. Manufacturing challenges will continue to accelerate advancements in technology and improve efficiency, safety and productivity as more factory operators incorporate real-time data analytics and artificial intelligence (AI). SEMI: Will suppliers continue to explore new avenues for smart manufacturing technologies and what are their growth opportunities?Kaufman: Yes, definitely. The sector has already changed, with COVID-19 bringing both opportunities and challenges. Industry leaders are facing new pressure, with sudden materials shortages, drops in demand and worker unavailability. The growth opportunities for manufacturing are likely to be digital, as already evident in the immediate response to the crisis. Industry 4.0 solutions will be crucial to increase end-to-end supply-chain transparency, automation and data integration. QualityLine manufacturing analytics have improved key manufacturing performance metrics. For example, based on customer feedback, we’ve increased production yield by 30%, saving some of our customers millions of dollars. Improvements like this can help suppliers withstand pandemics.Dr. Eyal Kaufman, Founder and CEO at QualityLine, has senior management experience and over 25 years of expertise in business development, marketing, finance, operations, engineering and quality management at leading industrial companies. Prior to QualityLine, he served as VP of Mobileye, Cardo Systems, and Medisim Ltd., as well as CEO of OnTheGo Systems. Eyal holds a Ph.D. from California Intercontinental University, an MBA from City University of New York and a BSc. from the Technion in Israel.The SEMI SMART Manufacturing Initiative is a global effort to promote awareness and interest about smart manufacturing with focus on delivering industry-recognized best-in-class programs and services to enable members to maximize product quality, productivity and cost improvements through smart manufacturing. Activities are focused on building out core capabilities to enable smart manufacturing across the microelectronics supply chain.MADEin4 is a consortium of 47 partners from 10 countries connecting the full range of supply chain: from semiconductor equipment manufacturers and system-integrating metrology companies to RTOS and key applications such as the automotive industry. The MADEin4 Project develops next generation metrology tools, machine learning methods and applications in support of Industry 4.0 high volume manufacturing in the semiconductor manufacturing industry.Serena Brischetto is a senior manager of marketing and communications at SEMI Europe.